Fast and accurate large-scale stereo reconstruction using variational methods
Kuschk G, Cremers D (2013)
Publication Type: Conference contribution
Publication year: 2013
Publisher: Institute of Electrical and Electronics Engineers Inc.
Pages Range: 700-707
Conference Proceedings Title: Proceedings of the IEEE International Conference on Computer Vision
Event location: AUS
ISBN: 9781479930227
Abstract
This paper presents a fast algorithm for high-accuracy large-scale outdoor dense stereo reconstruction of man-made environments. To this end, we propose a structure-adaptive second-order Total Generalized Variation (TGV) regularization which facilitates the emergence of planar structures by enhancing the discontinuities along building facades. As data term we use cost functions which are robust to illumination changes arising in real world scenarios. Instead of solving the arising optimization problem by a coarse-to-fine approach, we propose a quadratic relaxation approach which is solved by an augmented Lagrangian method. This technique allows for capturing large displacements and fine structures simultaneously. Experiments show that the proposed augmented Lagrangian formulation leads to a speedup by about a factor of 2. The brightness-adaptive second-order regularization produces sub-disparity accurate and piecewise planar solutions, favoring not only fronto-parallel, but also slanted planes aligned with brightness edges in the resulting disparity maps. The algorithm is evaluated and shown to produce consistently good results for various data sets (close range indoor, ground based outdoor, aerial imagery). © 2013 IEEE.
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Germany (DE)How to cite
APA:
Kuschk, G., & Cremers, D. (2013). Fast and accurate large-scale stereo reconstruction using variational methods. In Proceedings of the IEEE International Conference on Computer Vision (pp. 700-707). AUS: Institute of Electrical and Electronics Engineers Inc..
MLA:
Kuschk, Georg, and Daniel Cremers. "Fast and accurate large-scale stereo reconstruction using variational methods." Proceedings of the 2013 14th IEEE International Conference on Computer Vision Workshops, ICCVW 2013, AUS Institute of Electrical and Electronics Engineers Inc., 2013. 700-707.
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